This week we're bringing the practical and the principled together. Picking the right AI project to start with is one of the most consequential decisions you'll make — and doing it responsibly is just as important as doing it strategically. Let's get into both.
⚡ Quick Wins This Week
The #1 Mistake Companies Make Choosing Their First AI Project. It's not choosing the wrong vendor. It's not underestimating the budget. The number one mistake is choosing a project that's too ambitious — one that requires custom builds, clean data you don't have, and organizational buy-in you haven't built yet. This week we're breaking down why that happens and how to choose a starting point that actually builds momentum.
Ethical Red Flags: What Leaders Should Watch For During AI Rollout. Most ethical AI failures don't look like failures at first. They look like speed. Vendor urgency. Skipped steps. Feedback nobody asked for. This week we're covering the specific red flags every leader should be watching for during AI rollout — and what responsible leadership looks like at each one.
The #1 Mistake Companies Make Choosing Their First AI Project
Here's the story that plays out constantly in organizations of every size.
Leadership gets excited about AI. They bring in a vendor. The vendor shows an impressive demo of a custom-built solution that would transform a core business process end-to-end. It's ambitious. It's exciting. It's exactly the kind of thing that gets approval in a room full of optimism.
Eighteen months and $200,000 later, the system is partially implemented, adoption is at 15%, and the original vendor is now billing for "additional scope."
The mistake wasn't the intention. It was the starting point.
Custom AI builds require clean, structured data you probably don't have yet. They require internal technical resources to manage the integration. They require organizational readiness that takes months to develop. And they require enough runway to absorb the inevitable surprises — which a first project almost never has.
The leaders who build successful AI programs don't start with the most ambitious use case. They start with the most achievable one — what we call the quick win.
A good first AI project has four characteristics:
- Clear ROI. Not "this will improve efficiency broadly" — but "this specific process costs us X hours per week at Y cost, and automation can reduce that by Z percent." Measurable before you start, verifiable after.
- Available, reasonably clean data. You don't need perfect data. You need data that exists, is accessible, and doesn't require a six-month cleanup project before the AI can touch it.
- Enthusiastic users. Volunteers, not conscripts. A small team that wants this to work will outperform a large team that was told to use it every single time.
- Off-the-shelf solution. Your first project should use a tool that already exists, not one that needs to be built. Custom builds are for organizations that have already proven AI works in their environment. You're not there yet — and that's fine.
The crawl-walk-run philosophy exists for a reason. Crawl means learning whether AI works in your specific environment before you commit to scaling it. The organizations that skip straight to run are the ones that end up in expensive failure conversations with their CFOs.
Start small. Prove the concept. Earn the right to be ambitious.
Ethical Red Flags: What Leaders Should Watch For During AI Rollout
Ethical AI failures rarely announce themselves. They accumulate quietly — in decisions that felt reasonable at the time, in shortcuts taken under deadline pressure, in feedback that nobody created a channel to hear.
Here are the five red flags every leader should be actively watching for:
- Biased outcomes surfacing in the data. If your AI tool is producing recommendations or decisions that consistently disadvantage certain groups — by role, demographic, tenure, or any other pattern — that's not a calibration issue. That's a bias issue. The responsible response is to pause, investigate the training data and model logic, and not resume deployment until you understand what's driving the pattern and have addressed it.
- "We can't really explain why it recommended that." If your team can't explain how the AI arrived at a consequential decision, you don't have transparency — you have a black box making choices that affect real people. Explainability isn't optional for high-stakes decisions. If a vendor can't tell you how their model works in plain language, that's a red flag about the vendor, not just the technology.
- Data privacy steps getting compressed under timeline pressure. "We'll do the full privacy review after launch" is one of the most dangerous sentences in AI implementation. Data handling, storage, consent, and regulatory compliance are not post-launch activities. If your timeline doesn't have room for them, your timeline is wrong — not your privacy requirements.
- Vendor pressure to move faster than your organization is ready. A vendor who's pushing for enterprise-wide deployment before you've completed a contained pilot doesn't understand how organizations adopt technology — or doesn't care, because their incentive is contract size, not your success. Responsible vendors support crawl-walk-run. They welcome go/no-go decision points. If yours doesn't, that tells you something important.
- Employee feedback with nowhere to go. If your team is surfacing concerns — about accuracy, fairness, workload impact, or anything else — and those concerns aren't being documented, escalated, and acted on, you've created a culture where people learn that raising issues doesn't matter. That's how small problems become large failures. Build a clear channel for AI concerns before you launch, not after something goes wrong.
The pattern across all five: ethical failures are almost always preceded by someone knowing something was off and not having a structure to act on it. Your job as a leader is to build that structure before you need it.
🔍 Deep Dive: Connecting the Two
Here's the through-line between choosing the right first project and watching for ethical red flags: both are about making decisions before the pressure hits.
The leaders who pick too-ambitious first projects almost always do it in a moment of excitement, before the hard realities of data quality and organizational readiness become clear. The leaders who end up with ethical failures almost always had early signals — a bias flag, a skipped review, a team member who raised something — that got set aside because the project had momentum.
In both cases, the error isn't a lack of good intentions. It's a lack of deliberate structure that forces the right questions before the decision is made.
A realistic first project scope and a clear ethical red flag framework both serve the same purpose: they give you a way to make principled decisions when the environment is pushing you toward fast ones.
This is the discipline that separates leaders who build sustainable AI programs from those who launch impressive initiatives that quietly collapse. The technology is available to everyone. The discipline isn't.
✅ Action Item
Two quick exercises this week — one for each topic:
- For project selection: Write down your top three AI use case candidates. For each one, honestly rate it on the four quick win criteria: clear ROI, available data, enthusiastic users, off-the-shelf solution. The one that scores highest across all four is your starting point — not the most exciting one, the most achievable one.
- For ethical readiness: Ask yourself which of the five red flags you currently have no formal structure to catch. Pick one and decide this week what that structure looks like — even if it's just a standing agenda item in your next team meeting.
💡 NRM Spotlight
If the project selection piece is hitting home — you have ideas but aren't sure where to start, or you've already started and aren't sure if you chose the right use case — the AI Strategy Assessment was built for exactly this moment.
It gives you a personalized 15-page roadmap based on your specific organization: your readiness across five dimensions, your highest-value starting points, and a realistic picture of what it takes to move from pilot to production. No generic advice. No hype. Just a clear view of where you are and what to do next.
If you're earlier in the process and want the foundational framing before you make any decisions, Module One of AI Leadership Essentials is free — no credit card, no commitment.
Coming Next Week
Next week we're moving from project selection to project evaluation — with a systematic framework for scoring and ranking AI use cases so gut feelings stop driving decisions.
And we're looking at what it actually takes to build a culture of responsible AI use — not a policy document, but a living set of behaviors that leaders model and teams follow.
Both in your inbox next week.
Until then — start achievable, stay principled. ~ Nikki